Student Courses Recommendation Using Ant Colony Optimization

Sobecki, Janusz; Tomczak, Jakub M.

doi:10.1007/978-3-642-12101-2_14

Cited by 27 publications

(21 citation statements)

References 9 publications

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“…In some case studies, authors choose to apply computational, intelligence-based algorithms to reach a degree of automatic advising by combining genetic algorithms with decision trees for developing the short-term curricular schedule, as well as by combining perception marks with the registered courses [41] or by assisting in data mining and intelligent adaptive fuzzy logic for implementing an elective course suggestion system [44]. In paper [37], the authors present recommendations of student courses using ant colony optimization and concluded that their solution is promising, since it overcomes most of the disadvantages of classical approaches based on collaborative filtering in terms of performance. AAS proposed in [24] employ recommendations based on decision support tools, while other authors [33,34], use expert systems combined with semantic infostructures [22,28,47], or database management systems [6].…”

Section: Selecting Coursesmentioning

confidence: 99%

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Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

2017

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This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Academic Advising Systems (AAS) and its impact on learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of AAS in generic educational strategic planning. The researchers examined the literature on experimental case studies conducted in the domain during the past ten years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Search terms identified 98 mature pieces of research work, but inclusion criteria limited the key studies to 43. The authors analyzed the research questions, methodology, and findings of these published papers and categorized them accordingly. The results have highlighted three distinct major directions of the AAS empirical research. This paper discusses the emerged added value of AAS research and highlights the significance of further implications. Finally, the authors set their thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.

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Section: Selecting Coursesmentioning

confidence: 99%

“…• Authors in [37] concluded that Ant Colony Optimization-based method is promising, since it enables students to overcome of the disadvantages of classical approaches and gives higher values of performance measures.…”

Section: Hybridmentioning

confidence: 99%

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

2017

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“…In Intelligent Tutoring Systems a learner can be described with data like: knowledge, interests, personal traits, learning styles, cognitive abilities, level of actual knowledge. This allows adaptivity by modifying learning scenario [11], adjusting type of learning material to student's preferences based on learning styles [27], [24] or recommending courses connected with learner's interests [21]. In [23] learner's behaviour (correctness of solving tasks) is modelled with logical rules of the form if-then.…”

Section: Related Workmentioning

confidence: 99%

A conception for use of user profile to prediction learning effects in Intelligent Tutoring Systems

Kozierkiewicz

Bernacki

2015

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)

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Intelligent Tutoring Systems (ITS) offer adaptivity to user abilities, personal character trait, learning styles and preferences. The user modelling is one of the major factors that can influence an adaptivity. The content and structure of user profile should allow to recommend learning material suitable for student's needs. In this work a user profile is designed and a method for predicting learner's abilities is proposed. We use a Naive Bayes classifier in order to predict student's learning results. A prediction of user's abilities could be very useful for determining an initial learning scenario or assigning a student to a suitable collaborative learning group.

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“…In such systems, a supply of service is recommended to users. There are some solutions [15] based on ACO for collaborating filtering problem. Subgraph finding such as Clique, is another problem that can be issued by the help of ACO [16].…”

Section: Introductionmentioning

confidence: 99%